A new chromatographic response function with automatically adapting weight factor for automated method development.

A critical factor for automated method development in chromatography is the maximization or minimization of an objective function describing the quality (and speed) of the separation. In chromatography, this function is commonly referred to as a chromatographic response function (CRF). Many CRFs have previously been introduced, but many have unfavourable properties such as featuring multiple optima, insufficient discriminatory power, and a too strong dependence on the weight factors needed to balance resolution and time penalty components. To overcome these problems, the present study introduces a new type of CRF wherein the relative weight of the time penalty term is a self-adaptive function of the separation quality. The ability to unambiguously identify the optimal gradient settings of this newly proposed CRF is compared to that of some of the most frequently used CRFs in a study covering 100 randomly composed in silico samples. Doing so, the new CRF is found to flawlessly lead to the correct solution (=linear gradient parameters providing the highest resolution in the shortest potential time) in 100 % of the cases, while the most frequently used literature CRFs were off-target for about 50 to 60 % of the samples, even when considering the availability of spectral peak shape data. Some slight alterations to the proposed CRF are introduced and discussed as well.

Improved assay development of pharmaceutical modalities using feedback-controlled liquid chromatography optimization.

Development of meaningful and reliable analytical assays in the (bio)pharmaceutical industry can often be challenging, involving tedious trial and error experimentation. In this work, an automated analytical workflow using an AI-based algorithm for streamlined method development and optimization is presented. Chromatographic methods are developed and optimized from start to finish by a feedback-controlled modeling approach using readily available LC instrumentation and software technologies, bypassing manual user intervention. With the use of such tools, the time requirement of the analyst is drastically minimized in the development of a method. Herein key insights on chromatography system control, automatic optimization of mobile phase conditions, and final separation landscape for challenging multicomponent mixtures are presented (e.g., small molecules drug, peptides, proteins, and vaccine products) showcased by a detailed comparison of a chiral method development process. The work presented here illustrates the power of modern chromatography instrumentation and AI-based software to accelerate the development and deployment of new separation assays across (bio)pharmaceutical modalities while yielding substantial cost-savings, method robustness, and fast analytical turnaround.

Left ventricular global longitudinal strain using a novel fully automated method: A head-to-head comparison with a manual layer-specific strain and establishment of normal reference ranges.

BACKGROUND: A novel automated method for measuring left ventricular (LV) global longitudinal strain (GLS) along the endocardium has advantages in terms of its rapid application and excellent reproducibility. However, it remains unclear whether the available normal range for conventional GLS using the manual method is applicable to the automated GLS method. This study aimed to compare automated GLS head-to-head with manual layer-specific GLS, and to identify whether a specialized normal reference range for automated GLS is needed and explore the main determinants. METHODS: In total, 1683 healthy volunteers (men, 43%; age, 18-80 years) were prospectively enrolled from 55 collaborating laboratories. LV GLS was measured using both manual layer-specific and automated methods. RESULTS: Automated GLS was higher than endocardial, mid-myocardial, and epicardial GLS. Women had a higher automated GLS than men. GLS had no significant age dependency in men, but first increased and then decreased with age in women. Accordingly, sex- and age-specific normal ranges for automated GLS were proposed. Moreover, GLS appeared to have different burdens in relation to dominant determinants between the sexes. GLS in men showed no dominant determinants; however, GLS in women correlated with age, body mass index, and heart rate. CONCLUSIONS: Using the novel automated method, was LV GLS higher than when using the manual GLS method. The normal ranges of automated GLS stratified according to sex and age were provided, with dominant determinants showing sex disparities that require full consideration in clinical practice.

A general and simple automated impervious surface mapping approach based on three-dimensional texture features (3DTF) using fine spatial resolution remotely sensed imagery.

The mapping of impervious surfaces using remote sensing techniques offer essential technical support for sustainable development objectives and safeguard the environment. In this study, we developed an automated method without training samples for mapping impervious surfaces using texture features. The different aggregated impervious surface patterns and distributions in study areas of Site A-C in China (Beijing, Huainan, Jinhua) were considered. The Site D-E in Dubai and Tehran, surrounded with deserts in arid areas. They were selected to develop and evaluate the performance of the proposed automated method. The texture features of the Contrast, Gabor wavelets, and secondary texture extraction (Con_Gabor) derived from Sentinel-2 images at each site were used to construct the three-dimensional texture features (3DTF) of impervious surfaces. The 3DTF-combined K-means classifier was used to automatically map the impervious surfaces. The results showed that the overall accuracies of mapping impervious surface were 91.15 %, 89.75 %, and 91.90 % in Site A-C. The overall accuracies of mapping impervious surface were 90.95 %, 91.45 % and 88.23 % in rural areas. The distributions of impervious surface on automated method, GHS-BUILT-S and ESA WorldCover were similar in study areas. The automated method for mapping impervious surfaces performed as well as the artificial neural network (ANN) and Random Forest (RF), and the advantage of not requiring training samples. The automated method was tested in the in Dubai and Tehran. The overall accuracies of the automatic method for mapping impervious surfaces >89 % at Site D-E, and >88 % at rural area. In addition, the 3DTF was proved as the simplest and most effective feature combination to map impervious surfaces. The impervious surface mapped using the automated method was robust across bands, seasons and sensors. However, further evaluation is necessary to assess the effectiveness of automated methods using high spatial resolution images for mapping impervious surface in complex areas.

Automated measurement of lamellar thickness in human bone using polarized light microscopy.

Lamellar bone formed in individuals with moderate and severe osteogenesis imperfecta (OI) is often composed of lamellae that are structurally abnormal. Measuring the thickness of these lamellae can be helpful in assessing the effect of specific collagen and collagen-related mutations on OI bone synthesis. Manual measurement of lamellar thicknesses in large quantities is very time consuming. The method for automated measurement described in this article utilizes an image processing script to identify the average thickness of multiple lamellae automatically from histologic images of bone. This allows for faster measurements that are less prone to human error and can account for variability in the thickness of a lamella along its length.•OI and control bone samples are prepared per the glycol methacrylate resin (JB-4 plastic) technique and viewed using polarized light microscopy.•Ideal bone regions for measurement are identified using specific qualitative criteria designed to ensure uniform and accurate thickness measurements.•The method was validated with dataset containing 211 lamellae from control bone and 212 lamellae from OI bone.

Evaluation of a semi-automated approach for FDG PET image analysis for routine clinical application in patients with multiple myeloma.

BACKGROUND: FDG PET/CT is a tool for assessing response to therapy in various cancers, and may provide an earlier biomarker of clinical response. We developed a novel semi-automated approach for analyzing FDG PET/CT images in patients with multiple myeloma (MM) to standardize FDG PET application. METHODS: Patients (n = 8) with relapsed/refractory MM from the Phase 2 study (NCT02899052) of venetoclax plus carfilzomib and dexamethasone underwent FDG PET/CT at baseline and up to two timepoints during treatment. Images were processed using an established automated segmentation algorithm, with the modification that a red marrow region in an unaffected lumbar vertebra was used to define background standardized uptake value normalized to lean body mass (SUL) threshold above which uptake was considered disease-specific uptake. This approach was compared to lesion segmentation, and to International Myeloma Working Group (IMWG) response criteria, including minimal residual disease (MRD). RESULTS: The two FDG PET analysis techniques agreed on evaluation of patient-level SULpeak for 67% of scans. In the metabolic response assessment per PET Response Criteria in Solid Tumors (PERCIST), the two techniques agreed in 75% of patients. Differences between techniques occurred in low-uptake lesions due to greater reader sensitivity to lesions with uptake marginally above background. PERCIST outcomes were generally in agreement with IMWC and MRD. CONCLUSIONS: This semi-automated analysis was in high agreement with standard approaches for detecting response to MM therapy. This proof-of-concept study suggests that larger studies should be conducted to confirm how FDG PET analysis may aid early response detection in MM.

An automated method to discover true events and classification of intracellular Ca2+ profiles for endothelium in situ injury assay.

Introduction: Endothelial cells (ECs), being located at the interface between flowing blood and vessel wall, maintain cardiovascular homeostasis by virtue of their ability to integrate chemical and physical cues through a spatio-temporally coordinated increase in their intracellular Ca2+ concentration ([Ca2+]i). Endothelial heterogeneity suggests the existence of spatially distributed functional clusters of ECs that display different patterns of intracellular Ca2+ response to extracellular inputs. Characterizing the overall Ca2+ activity of the endothelial monolayer in situ requires the meticulous analysis of hundreds of ECs. This complex analysis consists in detecting and quantifying the true Ca2+ events associated to extracellular stimulation and classifying their intracellular Ca2+ profiles (ICPs). The injury assay technique allows exploring the Ca2+-dependent molecular mechanisms involved in angiogenesis and endothelial regeneration. However, there are true Ca2+ events of nearly undetectable magnitude that are almost comparable with inherent instrumental noise. Moreover, undesirable artifacts added to the signal by mechanical injury stimulation complicate the analysis of intracellular Ca2+ activity. In general, the study of ICPs lacks uniform criteria and reliable approaches for assessing these highly heterogeneous spatial and temporal events. Methods: Herein, we present an approach to classify ICPs that consists in three stages: 1) identification of Ca2+ candidate events through thresholding of a feature termed left-prominence; 2) identification of non-true events, known as artifacts; and 3) ICP classification based upon event temporal location. Results: The performance assessment of true-events identification showed competitive sensitivity = [0.9995, 0.9831], specificity = [0.9946, 0.7818] and accuracy = [0.9978, 0.9579] improvements of 2x and 14x, respectively, compared with other methods. The ICP classifier enhanced by artifact detection showed 0.9252 average accuracy with the ground-truth sets provided for validation. Discussion: Results indicate that our approach ensures sturdiness to experimental protocol maneuvers, besides it is effective, simple, and configurable for different studies that use unidimensional time dependent signals as data. Furthermore, our approach would also be effective to analyze the ICPs generated by other cell types, other dyes, chemical stimulation or even signals recorded at higher frequency.

Prognostic impact of artificial intelligence-based fully automated global circumferential strain in patients undergoing stress CMR.

AIMS: To determine whether fully automated artificial intelligence-based global circumferential strain (GCS) assessed during vasodilator stress cardiovascular (CV) magnetic resonance (CMR) can provide incremental prognostic value. METHODS AND RESULTS: Between 2016 and 2018, a longitudinal study included all consecutive patients with abnormal stress CMR defined by the presence of inducible ischaemia and/or late gadolinium enhancement. Control subjects with normal stress CMR were selected using a propensity score-matching. Stress-GCS was assessed using a fully automatic machine-learning algorithm based on featured-tracking imaging from short-axis cine images. The primary outcome was the occurrence of major adverse clinical events (MACE) defined as CV mortality or nonfatal myocardial infarction. Cox regressions evaluated the association between stress-GCS and the primary outcome after adjustment for traditional prognosticators. In 2152 patients [66 ± 12 years, 77% men, 1:1 matched patients (1076 with normal and 1076 with abnormal CMR)], stress-GCS was associated with MACE [median follow-up 5.2 (4.8-5.5) years] after adjustment for risk factors in the propensity-matched population [adjusted hazard ratio (HR), 1.12 (95% CI, 1.06-1.18)], and patients with normal CMR [adjusted HR, 1.35 (95% CI, 1.19-1.53), both P < 0.001], but not in patients with abnormal CMR (P = 0.058). In patients with normal CMR, an increased stress-GCS showed the best improvement in model discrimination and reclassification above traditional and stress CMR findings (C-statistic improvement: 0.14; NRI = 0.430; IDI = 0.089, all P < 0.001; LR-test P < 0.001). CONCLUSION: Stress-GCS is not a predictor of MACE in patients with ischaemia, but has an incremental prognostic value in those with a normal CMR although the absolute event rate remains low.

The impact of VITEK 2 implementation for identification and susceptibility testing of microbial isolates in a Brazilian public hospital.

Introduction. The use of automated systems in identification and susceptibility tests can improve antimicrobial therapy, and positively impact clinical outcomes with a decrease in antimicrobial resistance, hospitalization time, costs, and mortality.Aim. The aim of this study was to evaluate the clinical impact of an automated method for identification and susceptibility testing of microbial isolates.Methodology. This was a retrospective cross-sectional study aimed to analyse the results before and after the implementation period of a VITEK 2 system in a Brazilian university hospital. Based on data from medical records, patients with a positive culture of clinical samples from January to July 2017 (conventional method) and from August to December 2017 (automated method) were included in this study. Demographic data, hospitalization time, time interval between culture collection and results, culture results and site, susceptibility profile, minimum inhibitory concentration, and outcome data were evaluated. Chi-square and Fischer's tests were used in the analysis.Results. Of the total samples, 836 were considered valid by the inclusion criteria, with 219 patients before VITEK 2 system implementation group and 545 in the post-implementation group. The comparison between the two periods showed a reduction of 25 % of the time to culture reports, a decrease of 33.5 to 17.0 days of hospitalization, and a reduction in mortality from 44.3-31.0 %, respectively.Conclusion. The VITEK 2 system provided early access to appropriate antimicrobial therapy for patients and effected a positive clinical impact with a reduction in mortality and hospitalization time.

An automated method for thermal-optical separation of aerosol organic/elemental carbon for 13C analysis at the sub-µgC level: A comprehensive assessment.

We describe and thoroughly evaluate a method for 13C analysis in different fractions of carbonaceous aerosols, especially elemental carbon (EC). This method combines a Sunset thermal-optical analyzer and an isotope ratio mass spectrometer (IRMS) via a custom-built automated separation, purification, and injection system. Organic carbon (OC), EC, and other specific fractions from aerosol filter samples can be separated and analyzed automatically for 13C based on thermal-optical protocols (EUSAAR_2 in this study) at sub-µgC levels. The main challenges in isolating EC for 13C analysis are the possible artifacts during OC/EC separation, including the premature loss of EC and the formation of pyrolyzed OC (pOC) that is difficult to separate from EC. Since those artifacts can be accompanied with isotope fractionation, their influence on the stable isotopic composition of EC was comprehensively investigated with various test compounds. The results show that the thermal-optical method is relatively successful in OC/EC separation for 13C analysis. The method was further tested on real aerosols samples. For biomass-burning source samples, (partial) inclusion of pOC into EC has negligible influence on the 13C signature of EC. However, for ambient samples, the influence of pOC on the 13C signature of EC can be significant, if it is not well separated from EC, which is true for many current methods for measuring 13C on EC. A case study in Xi'an, China, where pOC is enriched in 13C compared to EC, shows that this can lead to an overestimate of coal and an underestimate of traffic emissions in isotope-based source apportionment.

Overestimation of corneal endothelial cell density by automated method in glaucomatous eyes with impaired corneal endothelial cells.

PURPOSE: To determine between-method differences in corneal endothelial cell parameters using center and automated methods of non-contact specular microscopy (CellCheck software of Konan, Inc.) in glaucomatous eyes. METHODS: We analyzed the central corneal endothelial cell density (ECD) of 245 glaucomatous eyes using center (ECD-Ce) and automated methods (ECD-Au). Based on the ECD-Ce, we allocated subjects to Groups 1 to 10 (at 250 cells/mm2 intervals) and evaluated the ECD, coefficient of variation in cell area (CV), and percentage of hexagonal cells (HEX). RESULTS: There was a close correlation (r = 0.91) between the ECD values measured using both methods. However, ECD-Au were significantly higher than those measured by the center method when ECD-Ce was less than 2500 (in Groups 1 to 8; P < 0.001 to P = 0.006). The regression equation of (ECD-Au-ECD-Ce) = 1028-0.397*ECD-Ce shows greater deviation in eyes with lower ECD, and this difference became 0 when ECD -Ce was 2593 cells/mm2. None of the 44 subjects with an ECD-Ce of < 1000 cells/mm2 recorded an ECD-Au < 1000 cells/mm2. Compared with the center method, the automated method had higher and lower median CV and HEX values, respectively (P < 0.001). The between-method differences in both CV and HEX were negatively correlated with ECD-Ce (r = -0.49, P < 0.001 and r = -0.25, P < 0.001, respectively). CONCLUSION: The automated method of the CellCheck software overestimates ECD in eyes with lower ECD values and may overlook risk of corneal decompensation.

A comparison of spectroscopic analysis methods for microplastics: Manual, semi-automated, and automated Fourier transform infrared and Raman techniques.

This study was conducted to establish the best practice for microplastic analysis by reducing the time demand and human bias and comparing the characteristics of µ-FTIR and Raman techniques. A manual analysis, semi-automated method, and fully automatic identification method were compared. Fully automated identification took the shortest time to analyze a whole filter paper (Ø25 mm), but its false positive identification rate was 80 ± 15%. The semi-automated analysis using spectrum profiling was suitable for all aspects of microplastic analysis. It was less time consuming than the manual analysis (manual: 6.1 ± 0.8 h, semi-automated: 4.0 ± 0.6 h), and 22 ± 12% more microplastic particles were detected using the semi-automated method compared to the manual analysis due to the reduction in false negative results. Raman microscopy was suitable for small microplastic (>5 µm) identification, although the Raman analysis took nine times longer than the semi-automated analysis.

An automated data cleaning method for Electronic Health Records by incorporating clinical knowledge.

BACKGROUND: The use of Electronic Health Records (EHR) data in clinical research is incredibly increasing, but the abundancy of data resources raises the challenge of data cleaning. It can save time if the data cleaning can be done automatically. In addition, the automated data cleaning tools for data in other domains often process all variables uniformly, meaning that they cannot serve well for clinical data, as there is variable-specific information that needs to be considered. This paper proposes an automated data cleaning method for EHR data with clinical knowledge taken into consideration. METHODS: We used EHR data collected from primary care in Flanders, Belgium during 1994-2015. We constructed a Clinical Knowledge Database to store all the variable-specific information that is necessary for data cleaning. We applied Fuzzy search to automatically detect and replace the wrongly spelled units, and performed the unit conversion following the variable-specific conversion formula. Then the numeric values were corrected and outliers were detected considering the clinical knowledge. In total, 52 clinical variables were cleaned, and the percentage of missing values (completeness) and percentage of values within the normal range (correctness) before and after the cleaning process were compared. RESULTS: All variables were 100% complete before data cleaning. 42 variables had a drop of less than 1% in the percentage of missing values and 9 variables declined by 1-10%. Only 1 variable experienced large decline in completeness (13.36%). All variables had more than 50% values within the normal range after cleaning, of which 43 variables had a percentage higher than 70%. CONCLUSIONS: We propose a general method for clinical variables, which achieves high automation and is capable to deal with large-scale data. This method largely improved the efficiency to clean the data and removed the technical barriers for non-technical people.

An automated ASPECTS method with atlas-based segmentation.

BACKGROUND AND PURPOSE: As a simple and reliable systematic method to evaluate the early ischemic changes in the blood supply region of the middle cerebral artery of patients with ischemic stroke, the Alberta Stroke Program Early CT score (ASPECTS) can be used for rapid semi-quantitative evaluation of ischemic lesions, which is helpful to select potential candidates for intravenous and intra-arterial therapies, determine the thrombolytic effect and long-term prognosis. This method mainly relies on doctors' visual observation. However, due to different levels of doctor's experience, the poor inter-reader agreement may result in errors in the final ASPECTS. The purpose of this work was to propose an automated semi-quantitative method for the diagnosis of acute ischemic stroke based on non-contrast computed tomography (NCCT), to provide a reference for doctors in the diagnosis and evaluation. METHODS: NCCT data from a total of 90 patients were included for auto-ASPECTS training and testing. After preprocessing CT images, the regions of interest (ROI) for ASPECTS were labeled using atlas-based segmentation. The mean difference, mean ratio and brain density shifts (BDS) of the corresponding regions of the contralateral brain were used as the standard for quantitative analysis. The auto-ASPECTS method was developed and validated to predict early ischemic changes whose performance was evaluated by the agreement (accuracy) of predictions and consensus scores of two observers. RESULTS: A comparison was made among the results on mean difference, mean ratio, BDS and the combination of multiple parameters as the standard. The result of using BDS alone was relatively better than the result of using any other parameter alone or any combination of multiple parameters, and accuracy in the test set was 0.80. In the test set, accuracy with using different BDS thresholds increased by 6.67% compared with using the consistent BDS threshold. After dichotomy of auto-ASPECTS and consensus scores with the threshold of 7, the agreement of them was 83.3% and there was no significant difference between the two distributions (p = 0.344) in McNemar test. CONCLUSIONS: The proposed auto-ASPECTS method for NCCT images can provide useful information for early diagnosis and evaluation of patients with acute ischemic stroke (AIS).

Advantages of an Automated Method Compared With Manual Methods for the Quantification of Intraepidermal Nerve Fiber in Skin Biopsy.

Intraepidermal nerve fiber density (IENFD) measurements in skin biopsy are performed manually by 1-3 operators. To improve diagnostic accuracy and applicability in clinical practice, we developed an automated method for fast IENFD determination with low operator-dependency. Sixty skin biopsy specimens were stained with the axonal marker PGP9.5 and imaged using a widefield fluorescence microscope. IENFD was first determined manually by 3 independent observers. Subsequently, images were processed in their Z-max projection and the intradermal line was delineated automatically. IENFD was calculated automatically (fluorescent images automated counting [FIAC]) and compared with manual counting on the same fluorescence images (fluorescent images manual counting [FIMC]), and with classical manual counting (CMC) data. A FIMC showed lower variability among observers compared with CMC (interclass correlation [ICC] = 0.996 vs 0.950). FIMC and FIAC showed high reliability (ICC = 0.999). A moderate-to-high (ICC = 0.705) was observed between CMC and FIAC counting. The algorithm process took on average 15 seconds to perform FIAC counting, compared with 10 minutes for FIMC counting. This automated method rapidly and reliably detects small nerve fibers in skin biopsies with clear advantages over the classical manual technique.

Comparison of the StaRRsed Interliner device with Westergren method in erythrocyte sedimentation rate measurement.

INTRODUCTION: With recent advances in technology, many manual tests are being replaced by automated devices due to a wide range of advantages. One of these tests is the erythrocyte sedimentation rate (ESR) test that is used to determine inflammatory activity. This study aimed to evaluate the agreement between the Starrsed Interliner sedimentation device and the gold standard method, that is the Westergren method, used in ESR measurement. METHODS: One hundred fifty-one patients who presented to Gaziantep University Faculty of Medicine, Sahinbey Training and Research Hospital were included in this study. ESR values were measured simultaneously within 2 hours using the ESR analyzer Starrsed Interliner device and the gold standard method of measuring ESR, that is the Westergren method, from blood samples collected from the same patients in EDTA and citrate tubes. RESULTS: Agreement between the results from the Starrsed Interliner device and the Westergren method was evaluated using the Intraclass Correlation method. Consequently, a poor correlation was observed at values <20 mm/h, a moderate correlation was observed at values 20 to 80 mm/h and >80 mm/h, and an excellent correlation was observed when all results were considered. Method comparison was conducted according to the Passing-Bablok regression analysis (y = -1.50 + 0.75x) (P < .0001). The mean difference between the two methods was 10.1 according to the Bland-Altman analysis. CONCLUSIONS: Despite the advantages of the Starrsed Interliner device, such as lower laboratory workloads, lower costs and turnaround time, the difference between the two methods, as found in this study, may lead to different clinical interpretations for results in some patient.

Effective automated method for detection and suppression of muscle artefacts from single-channel EEG signal.

This Letter proposes an automated method for the detection and suppression of muscle artefacts (MAs) in the single-channel electroencephalogram (EEG) signal based on variational mode decomposition (VMD) and zero crossings count threshold criterion without the use of reference electromyogram (EMG). The proposed method involves three major steps: decomposition of the input EEG signal into two modes using VMD; detection of MAs based on zero crossings count thresholding in the second mode; retention of the first mode as MAs-free EEG signal only after detection of MAs in the second mode. The authors evaluate the robustness of the proposed method on a variety of EEG and EMG signals taken from publicly available databases, including Mendeley database, epileptic Bonn database and EEG during mental arithmetic tasks database (EEGMAT). Evaluation results using different objective performance metrics depict the superiority of the proposed method as compared to existing methods while preserving the clinical features of the reconstructed EEG signal.

Semi-Automated Assessment of Human Islet Viability Predicts Transplantation Outcomes in a Diabetic Mouse Model.

In clinical and experimental human pancreatic islet transplantations, establishing pretransplant assessments that accurately predict transplantation outcomes is crucial. Conventional in vitro viability assessment that relies on manual counting of viable islets is a routine pretransplant assessment. However, this method does not correlate with transplantation outcomes; to improve the method, we recently introduced a semi-automated method using imaging software to objectively determine area-based viability. The goal of the present study was to correlate semi-automated viability assessment with posttransplantation outcomes of human islet transplantations in diabetic immunodeficient mice, the gold standard for in vivo functional assessment of isolated human islets. We collected data from 61 human islet isolations and 188 subsequent in vivo mouse transplantations. We assessed islet viability by fluorescein diacetate and propidium iodide staining using both the conventional and semi-automated method. Transplantations of 1,200 islet equivalents under the kidney capsule were performed in streptozotocin-induced diabetic immunodeficient mice. Among the pretransplant variables, including donor factors and post-isolation assessments, viability measured using the semi-automated method demonstrated a strong influence on in vivo islet transplantation outcomes in multivariate analysis. We calculated an optimized cutoff value (96.1%) for viability measured using the semi-automated method and showed a significant difference in diabetes reversal rate for islets with viability above this cutoff (77% reversal) vs. below this cutoff (49% reversal). We performed a detailed analysis to show that both the objective measurement and the improved area-based scoring system, which distinguished between small and large islets, were key features of the semi-automated method that allowed for precise evaluation of viability. Taken together, our results suggest that semi-automated viability assessment offers a promising alternative pretransplant assessment over conventional manual assessment to predict human islet transplantation outcomes.

Comparison of the recommended colistin susceptibility testing methods with colistin gradient strips and semi-automated method for antimicrobial-resistant non-fermenting rods.

An increased frequency of multidrug-resistant non-fermenting rods isolation has resulted in the excessive use of colistin - often the last chance antimicrobial. However, determination of colistin susceptibility is difficult, mainly because of its structure and limited diffusion properties. This study was performed to compare colistin susceptibility testing among Pseudomonas aeruginosa (n = 49) and Acinetobacter baumannii (n = 49) strains. Four methods were applied: colistin gradient strips (Liofilchem, Italy), semi-automated method Phoenix BD (Becton Dickinson, USA) and two broth microdilution methods: SensiTest Colistin (Liofilchem, Italy) and MICRONAUT MIC-Strip (MERLIN Diagnostika GmbH, Germany). Data were analyzed by comparison of MIC values and strains susceptibility interpretation criteria (resistant and sensitive, respectively). The same interpretation results were obtained for 46 (93.9%) P. aeruginosa and 37 (75.5%) A. baumannii isolates in all of the applied methods. Using broth microdilution methods, the same interpretation was obtained for 48 (98.0%) P. aeruginosa and 42 (85.7%) A. baumannii isolates. The results obtained by colistin gradient strips usually confirm the results of broth microdilution tests for P. aeruginosa isolates, the automated method is in turn less labor-intensive. However, MIC values, obtained with their use, are less precise because of the antibiotic dilutions limited to only several concentrations. The results underline the importance of choosing of the appropriate type of method, also among those recommended and commercially available.